Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics.

Overview

Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics.

Build Status

By Andres Milioto @ University of Bonn.

(for the new Pytorch version, go here)

Image of cityscapes Cityscapes Urban Scene understanding.

Image of Persons Person Segmentation

Image of cwc Crop vs. Weed Semantic Segmentation.

Description

This code provides a framework to easily add architectures and datasets, in order to train and deploy CNNs for a robot. It contains a full training pipeline in python using Tensorflow and OpenCV, and it also some C++ apps to deploy a frozen protobuf in ROS and standalone. The C++ library is made in a way which allows to add other backends (such as TensorRT and MvNCS), but only Tensorflow and TensorRT are implemented for now. For now, we will keep it this way because we are mostly interested in deployment for the Jetson and Drive platforms, but if you have a specific need, we accept pull requests!

The networks included is based of of many other architectures (see below), but not exactly a copy of any of them. As seen in the videos, they run very fast in both GPU and CPU, and they are designed with performance in mind, at the cost of a slight accuracy loss. Feel free to use it as a model to implement your own architecture.

All scripts have been tested on the following configurations:

  • x86 Ubuntu 16.04 with an NVIDIA GeForce 940MX GPU (nvidia-384, CUDA9, CUDNN7, TF 1.7, TensorRT3)
  • x86 Ubuntu 16.04 with an NVIDIA GTX1080Ti GPU (nvidia-375, CUDA9, CUDNN7, TF 1.7, TensorRT3)
  • x86 Ubuntu 16.04 and 14.04 with no GPU (TF 1.7, running on CPU in NHWC mode, no TensorRT support)
  • Jetson TX2 (full Jetpack 3.2)

We also provide a Dockerfile to make it easy to run without worrying about the dependencies, which is based on the official nvidia/cuda image containing cuda9 and cudnn7. In order to build and run this image with support for X11 (to display the results), you can run this in the repo root directory (nvidia-docker should be used instead of vainilla docker):

  $ docker pull tano297/bonnet:cuda9-cudnn7-tf17-trt304
  $ nvidia-docker build -t bonnet .
  $ nvidia-docker run -ti --rm -e DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix -v $HOME/.Xauthority:/home/developer/.Xauthority -v /home/$USER/data:/shared --net=host --pid=host --ipc=host bonnet /bin/bash

-v /home/$USER/data:/share can be replaced to point to wherever you store the data and trained models, in order to include the data inside the container for inference/training.

Deployment

  • /deploy_cpp contains C++ code for deployment on robot of the full pipeline, which takes an image as input and produces the pixel-wise predictions as output, and the color masks (which depend on the problem). It includes both standalone operation which is meant as an example of usage and build, and a ROS node which takes a topic with an image and outputs 2 topics with the labeled mask and the colored labeled mask.

  • Readme here

Training

  • /train_py contains Python code to easily build CNN Graphs in Tensorflow, train, and generate the trained models used for deployment. This way the interface with Tensorflow can use the more complete Python API and we can easily work with files to augment datasets and so on. It also contains some apps for using models, which includes the ability to save and use a frozen protobuf, and to use the network using TensorRT, which reduces the time for inference when using NVIDIA GPUs.

  • Readme here

Pre-trained models

These are some models trained on some sample datasets that you can use with the trainer and deployer, but if you want to take time to write the parsers for another dataset (yaml file with classes and colors + python script to put the data into the standard dataset format) feel free to create a pull request.

If you don't have GPUs and the task is interesting for robots to exploit, I will gladly train it whenever I have some free GPU time in our servers.

  • Cityscapes:

    • 512x256 Link
    • 768x384 Link (inception-like model)
    • 768x384 Link (mobilenets-like model)
    • 1024x512 Link
  • Synthia:

  • Persons (+coco people):

  • Crop-Weed (CWC):

License

This software

Bonnet is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

Bonnet is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

Pretrained models

The pretrained models with a specific dataset keep the copyright of such dataset.

Citation

If you use our framework for any academic work, please cite its paper.

@InProceedings{milioto2019icra,
author = {A. Milioto and C. Stachniss},
title = {{Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics using CNNs}},
booktitle = {Proc. of the IEEE Intl. Conf. on Robotics \& Automation (ICRA)},
year = 2019,
codeurl = {https://github.com/Photogrammetry-Robotics-Bonn/bonnet},
videourl = {https://www.youtube.com/watch?v=tfeFHCq6YJs},
}

Our networks are strongly based on the following architectures, so if you use them for any academic work, please give a look at their papers and cite them if you think proper:

Other useful GitHub's:

  • OpenAI Checkpointed Gradients. Useful implementation of checkpointed gradients to be able to fit big models in GPU memory without sacrificing runtime.
  • Queueing tool: Very nice queueing tool to share GPU, CPU and Memory resources in a multi-GPU environment.
  • Tensorflow_cc: Very useful repo to compile Tensorflow either as a shared or static library using CMake, in order to be able to compile our C++ apps against it.

Contributors

Milioto, Andres

Special thanks to Philipp Lottes for all the work shared during the last year, and to Olga Vysotka and Susanne Wenzel for beta testing the framework :)

Acknowledgements

This work has partly been supported by the German Research Foundation under Germany's Excellence Strategy, EXC-2070 - 390732324 (PhenoRob). We also thank NVIDIA Corporation for providing a Quadro P6000 GPU partially used to develop this framework.

TODOs

  • Merge Crop-weed CNN with background knowledge into this repo.
  • Make multi-camera ROS node that exploits batching to make inference faster than sequentially.
  • Movidius Neural Stick C++ backends (plus others as they become available).
  • Inference node to show the classes selectively (e.g. with some qt visual GUI)
Owner
Photogrammetry & Robotics Bonn
Photogrammetry & Robotics Lab at the University of Bonn
Photogrammetry & Robotics Bonn
NER for Indian languages

CL-NERIL: A Cross-Lingual Model for NER in Indian Languages Code for the paper - https://arxiv.org/abs/2111.11815 Setup Setup a virtual environment Th

Akshara P 0 Nov 24, 2021
SHRIMP: Sparser Random Feature Models via Iterative Magnitude Pruning

SHRIMP: Sparser Random Feature Models via Iterative Magnitude Pruning This repository is the official implementation of "SHRIMP: Sparser Random Featur

Bobby Shi 0 Dec 16, 2021
TCTrack: Temporal Contexts for Aerial Tracking (CVPR2022)

TCTrack: Temporal Contexts for Aerial Tracking (CVPR2022) Ziang Cao and Ziyuan Huang and Liang Pan and Shiwei Zhang and Ziwei Liu and Changhong Fu In

Intelligent Vision for Robotics in Complex Environment 100 Dec 19, 2022
Benchmark for the generalization of 3D machine learning models across different remeshing/samplings of a surface.

Discretization Robust Correspondence Benchmark One challenge of machine learning on 3D surfaces is that there are many different representations/sampl

Nicholas Sharp 10 Sep 30, 2022
R-Drop: Regularized Dropout for Neural Networks

R-Drop: Regularized Dropout for Neural Networks R-drop is a simple yet very effective regularization method built upon dropout, by minimizing the bidi

756 Dec 27, 2022
This repository contains all code and data for the Inside Out Visual Place Recognition task

Inside Out Visual Place Recognition This repository contains code and instructions to reproduce the results for the Inside Out Visual Place Recognitio

15 May 21, 2022
USAD - UnSupervised Anomaly Detection on multivariate time series

USAD - UnSupervised Anomaly Detection on multivariate time series Scripts and utility programs for implementing the USAD architecture. Implementation

116 Jan 04, 2023
A Library for Modelling Probabilistic Hierarchical Graphical Models in PyTorch

A Library for Modelling Probabilistic Hierarchical Graphical Models in PyTorch

Korbinian Pöppel 47 Nov 28, 2022
Credit fraud detection in Python using a Jupyter Notebook

Credit-Fraud-Detection - Credit fraud detection in Python using a Jupyter Notebook , using three classification models (Random Forest, Gaussian Naive Bayes, Logistic Regression) from the sklearn libr

Ali Akram 4 Dec 28, 2021
Code for MSc Quantitative Finance Dissertation

MSc Dissertation Code ReadMe Sector Volatility Prediction Performance Using GARCH Models and Artificial Neural Networks Curtis Nybo MSc Quantitative F

2 Dec 01, 2022
This repo provides code for QB-Norm (Cross Modal Retrieval with Querybank Normalisation)

This repo provides code for QB-Norm (Cross Modal Retrieval with Querybank Normalisation) Usage example python dynamic_inverted_softmax.py --sims_train

36 Dec 29, 2022
Gym for multi-agent reinforcement learning

PettingZoo is a Python library for conducting research in multi-agent reinforcement learning, akin to a multi-agent version of Gym. Our website, with

Farama Foundation 1.6k Jan 09, 2023
[CVPR 2022] Thin-Plate Spline Motion Model for Image Animation.

[CVPR2022] Thin-Plate Spline Motion Model for Image Animation Source code of the CVPR'2022 paper "Thin-Plate Spline Motion Model for Image Animation"

yoyo-nb 1.4k Dec 30, 2022
Voice Conversion Using Speech-to-Speech Neuro-Style Transfer

This repo contains the official implementation of the VAE-GAN from the INTERSPEECH 2020 paper Voice Conversion Using Speech-to-Speech Neuro-Style Transfer.

Ehab AlBadawy 93 Jan 05, 2023
This is RFA-Toolbox, a simple and easy-to-use library that allows you to optimize your neural network architectures using receptive field analysis (RFA) and create graph visualizations of your architecture.

ReceptiveFieldAnalysisToolbox This is RFA-Toolbox, a simple and easy-to-use library that allows you to optimize your neural network architectures usin

84 Nov 23, 2022
FastReID is a research platform that implements state-of-the-art re-identification algorithms.

FastReID is a research platform that implements state-of-the-art re-identification algorithms.

JDAI-CV 2.8k Jan 07, 2023
Facial Action Unit Intensity Estimation via Semantic Correspondence Learning with Dynamic Graph Convolution

FAU Implementation of the paper: Facial Action Unit Intensity Estimation via Semantic Correspondence Learning with Dynamic Graph Convolution. Yingruo

Evelyn 78 Nov 29, 2022
Learning from Synthetic Data with Fine-grained Attributes for Person Re-Identification

Less is More: Learning from Synthetic Data with Fine-grained Attributes for Person Re-Identification Suncheng Xiang Shanghai Jiao Tong University Over

SunchengXiang 68 Dec 13, 2022
Pytorch Implementation of "Desigining Network Design Spaces", Radosavovic et al. CVPR 2020.

RegNet Pytorch Implementation of "Desigining Network Design Spaces", Radosavovic et al. CVPR 2020. Paper | Official Implementation RegNet offer a very

Vishal R 2 Feb 11, 2022
Unofficial PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners

Unofficial PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners This repository is built upon BEiT, thanks very much! Now, we on

Zhiliang Peng 2.3k Jan 04, 2023